On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems
This addresses the challenge of ensuring decision reliability in self-adaptive systems when integrating machine learning, which is incremental as it focuses on a specific combination of methods.
The paper investigates how using linear regression to reduce adaptation spaces in self-adaptive systems affects the reliability of formal verification methods like statistical model checking, providing a theoretical bound on this impact and evaluating it with a DeltaIoT scenario.
Recently, we have been witnessing an increasing use of machine learning methods in self-adaptive systems. Machine learning methods offer a variety of use cases for supporting self-adaptation, e.g., to keep runtime models up to date, reduce large adaptation spaces, or update adaptation rules. Yet, since machine learning methods apply in essence statistical methods, they may have an impact on the decisions made by a self-adaptive system. Given the wide use of formal approaches to provide guarantees for the decisions made by self-adaptive systems, it is important to investigate the impact of applying machine learning methods when such approaches are used. In this paper, we study one particular instance that combines linear regression to reduce the adaptation space of a self-adaptive system with statistical model checking to analyze the resulting adaptation options. We use computational learning theory to determine a theoretical bound on the impact of the machine learning method on the predictions made by the verifier. We illustrate and evaluate the theoretical result using a scenario of the DeltaIoT artifact. To conclude, we look at opportunities for future research in this area.